NOTE: fit-clusters requires installation with gin extras, e.g. pip install divik[gin]

fit-clusters is just one CLI executable that allows you to run DiviK algorithm, any other clustering algorithms supported by scikit-learn or even a pipeline with pre-processing.

Usage

CLI interface

There are two types of parameters:

1. --param - this way you can set the value of a parameter during

   fit-clusters executable launch, i.e. you can overwrite parameter provided

   in a config file or a default.

2. --config - this way you can provide a list of config files. Their

   content will be treated as a one big (ordered) list of settings. In case of

   conflict, the later file overwrites a setting provided by earlier one.

These go directly to the CLI.

Sample fit-clusters call:

The elaboration of all the parameters is included in Experiment configuration and Model setup.

Following parameters are available when launching experiments:

1. load_data.path - path to the file with data for clustering. Observations

   in rows, features in columns.

2. load_xy.path - path to the file with X and Y coordinates for the

   observations. The number of coordinate pairs must be the same as the number

   of observations. Only integer coordinates are supported now.

To use DiviK algorithm in the experiment, a config file must:

1. Import the algorithms to the scope, e.g.:

1. Point experiment which algorithm to use, e.g.:

1. Configure the algorithm, e.g.:

Below you can check sample configuration file, that sets up simple KMeans:

Below is the configuration file with full setup of DiviK. DiviK requires an automated clustering method for stop condition and a separate one for clustering. Here we use GAPSearch for stop condition and DunnSearch for selecting the number of clusters. These in turn require a KMeans method set for a specific distance method, etc.:

For a model to be used with fit-clusters, it needs to be marked as gin.configurable. While it is true for DiviK and remaining algorithms within divik package, scikit-learn requires additional setup.

1. Import helper module:

1. Point experiment which algorithm to use, e.g.:

1. Configure the algorithm, e.g.:

WARNING: Importing both scikit-learn and divik will result in an ambiguity when using e.g. KMeans. In such a case it is necesary to point specific algorithms by a full name, e.g. divik.cluster._kmeans._core.KMeans.

Below you can check sample configuration file, that sets up simple MeanShift:

scikit-learn Pipelines have a separate section to provide an additional explanation, even though these are part of scikit-learn.

1. Import helper module:

1. Import the algorithms into the scope:

1. Point experiment which algorithm to use, e.g.:

1. Configure the algorithms, e.g.:

1. Configure the pipeline:

1. (If needed) configure steps that require spatial coordinates:

Below you can check sample configuration file, that sets up simple Pipeline:

The fit-clusters executable can work with custom algorithms as well.

1. Mark an algorithm class gin.configurable at the definition time:

or when importing them from a library:

1. Define artifacts saving methods:

There are some default savers defined, which are compatible with lots of divik and scikit-learn algorithms, supporting things like:

• model pickling

• JSON summary saving

• labels saving (.npy, .csv)

A saver should be highly reusable and could be a pleasant contribution to the divik library.

1. In config, import the module which marks your algorithm configurable:

1. Continue with the algorithm setup and plumbing as in the previous scenarios

Tools within this package

• Clustering at your command line with fit-clusters

• Set of algorithm implementations for unsupervised analyses

  • Clustering

    • - hands-free clustering method with built-in feature selection

    • for selecting the number of clusters

    • for selecting the number of clusters

    • Modular with custom distance metrics and initializations

  • • data-driven feature selection

  • • - generates samples of fixed number of rows from given dataset, preserving groups proportion

    • - generates samples of random observations within boundaries of an original dataset, and preserving the rotation of the data

The recommended way to use this software is through . This is the most convenient way, if you want to use divik application.

To install latest stable version use:

Prerequisites for installation of base package:

• Python 3.6 / 3.7 / 3.8

• compiler capable of compiling the native C code and OpenMP support

You should have it already installed with GCC compiler, but if somehow not, try the following:

OpenMP is available as part of LLVM. You may need to install it with conda:

Having prerequisites installed, one can install latest base version of the package:

If you want to have compatibility with , you can install necessary extras with:

Note: Remember about \ before [ and ] in zsh shell.

You can install all extras with:

If you are using DiviK to run the analysis that could fail to fit RAM of your computer, consider disabling the default parallelism and switch to . It's easy to achieve through configuration:

• set all parameters named n_jobs to 1;

• set all parameters named allow_dask to True.

Note: Never set n_jobs>1 and allow_dask=True at the same time, the computations will freeze due to how multiprocessing and dask handle parallelism.

It can happen if the he gamred_native package (part of divik package) was compiled with different numpy ABI than scikit-learn. This could happen if you used different set of compilers than the developers of the scikit-learn package.

In such a case, a handler is defined to display the stack trace. If the trace comes from _matlab_legacy.py, the most probably this is the issue.

To resolve the issue, consider following the installation instructions once again. The exact versions get updated to avoid the issue.

Contribution guide will be developed soon.

Format the code with:

This software is part of contribution made by , rest of which is published .